Transformative advances in Artificial Intelligence are spurring the advent of true driverless self-driving cars. This incredible book provides original innovative approaches to explain how these autonomous vehicles are becoming increasingly viable and practical. Dr. Eliot is a renowned AI Insider and known for his practical application of AI. His popular podcast is heard globally. A serial entrepreneur and high-tech executive, he serves as the Executive Director of the Cybernetic Self-Driving Car Institute. Visit his web site www.ai-selfdriving-cars.guru for further information.
Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCV Key FeaturesBuild and train powerful neural network models to build an autonomous carImplement computer vision, deep learning, and AI techniques to create automotive algorithmsOvercome the challenges faced while automating different aspects of driving using modern Python libraries and architecturesBook Description Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving. By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries. What you will learnImplement deep neural network from scratch using the Keras libraryUnderstand the importance of deep learning in self-driving carsGet to grips with feature extraction techniques in image processing using the OpenCV libraryDesign a software pipeline that detects lane lines in videosImplement a convolutional neural network (CNN) image classifier for traffic signal signsTrain and test neural networks for behavioral-cloning by driving a car in a virtual simulatorDiscover various state-of-the-art semantic segmentation and object detection architecturesWho this book is for If you are a deep learning engineer, AI researcher, or anyone looking to implement deep learning and computer vision techniques to build self-driving blueprint solutions, this book is for you. Anyone who wants to learn how various automotive-related algorithms are built, will also find this book useful. Python programming experience, along with a basic understanding of deep learning, is necessary to get the most of this book.
"Artificial Intelligence and Its Transformative Impact on Society" delves into the profound influence AI exerts across diverse facets of human activity and governance. Beginning with a foundational understanding of artificial intelligence, the book progresses through detailed explorations of its implications for governance, economic landscapes, and ethical dilemmas, including bias and privacy concerns. Each chapter builds methodically on the last, examining AI’s role from autonomous systems and transportation to its effects on cultural heritage, mental health, and even international relations. This comprehensive guide not only illuminates the technical and practical aspects of AI but also addresses the broader societal, ethical, and existential risks associated with its advancement. Through its 20 chapters, this book provides readers with a nuanced perspective on the challenges and opportunities AI presents in shaping the future of society.
The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.
Better public policies can make the road smoother for self-driving vehicles and the society that soon will depend on them. Whether you find the idea of autonomous vehicles to be exciting or frightening, the truth is that they will soon become a significant everyday presence on streets and highways—not just a novel experiment attracting attention or giggles and sparking fears of runaway self-driving cars. The emergence of these vehicles represents a watershed moment in the history of transportation. If properly encouraged, this innovation promises not only to vastly improve road travel and generate huge benefits to travelers and businesses, but to also benefit the entire economy by reducing congestion and virtually eliminating vehicle accidents. The impacts of autonomous vehicles on land use, employment, and public finance are likely to be mixed. But widely assumed negative effects are generally overstated because they ignore plausible adjustments by the public and policymakers that could ameliorate them. This book by two transportation experts argues that policy analysts can play an important and constructive role in identifying and analyzing important policy issues and necessary steps to ease the advent of autonomous vehicles. Among the actions that governments must take are creating a framework for vehicle testing, making appropriate investments in the technology of highway networks to facilitate communication involving autonomous vehicles, and reforming pricing and investment policies to enable operation of autonomous vehicles to be safe and efficient. The authors argue that policymakers at all levels of government must address these and other issues sooner rather than later. Prompt and effective actions outlined in this book are necessary to ensure that autonomous vehicles will be safe and efficient when the public begins to adopt them as replacements for current vehicles.
Explainable AI for Autonomous Vehicles: Concepts, Challenges, and Applications is a comprehensive guide to developing and applying explainable artificial intelligence (XAI) in the context of autonomous vehicles. It begins with an introduction to XAI and its importance in developing autonomous vehicles. It also provides an overview of the challenges and limitations of traditional black-box AI models and how XAI can help address these challenges by providing transparency and interpretability in the decision-making process of autonomous vehicles. The book then covers the state-of-the-art techniques and methods for XAI in autonomous vehicles, including model-agnostic approaches, post-hoc explanations, and local and global interpretability techniques. It also discusses the challenges and applications of XAI in autonomous vehicles, such as enhancing safety and reliability, improving user trust and acceptance, and enhancing overall system performance. Ethical and social considerations are also addressed in the book, such as the impact of XAI on user privacy and autonomy and the potential for bias and discrimination in XAI-based systems. Furthermore, the book provides insights into future directions and emerging trends in XAI for autonomous vehicles, such as integrating XAI with other advanced technologies like machine learning and blockchain and the potential for XAI to enable new applications and services in the autonomous vehicle industry. Overall, the book aims to provide a comprehensive understanding of XAI and its applications in autonomous vehicles to help readers develop effective XAI solutions that can enhance autonomous vehicle systems' safety, reliability, and performance while improving user trust and acceptance. This book: Discusses authentication mechanisms for camera access, encryption protocols for data protection, and access control measures for camera systems. Showcases challenges such as integration with existing systems, privacy, and security concerns while implementing explainable artificial intelligence in autonomous vehicles. Covers explainable artificial intelligence for resource management, optimization, adaptive control, and decision-making. Explains important topics such as vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, remote monitoring, and control. Emphasizes enhancing safety, reliability, overall system performance, and improving user trust in autonomous vehicles. The book is intended to provide researchers, engineers, and practitioners with a comprehensive understanding of XAI's key concepts, challenges, and applications in the context of autonomous vehicles. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, information technology, and automotive engineering.
This book examines the fundamental concepts and principles of digital transformation and AI, including their historical development, and underlying technologies, and analyzes the opportunities arising from digital transformation and AI in different sectors, such as healthcare, finance, education, transportation, and governance. It provides a comprehensive overview of digital transformation and AI technologies and their current state of implementation. It also explores the potential challenges and risks associated with digital transformation and AI, including ethical considerations, job displacement, privacy concerns, biases, impact on inequality, social interactions, and the overall well-being of individuals and communities. Additionally, the books provides and discusses policy and regulatory frameworks that can effectively address the opportunities and challenges posed by digital transformation and AI leading to responsible AI. It also delves into impact of automation on the job market and workforce. The book concludes by proposing potential strategies for navigating opportunities and challenges of digital transformation and AI integration. It emphasizes the need for interdisciplinary collaboration among stakeholders, including policymakers, industry leaders, academia, and civil society, to develop a comprehensive approach towards harnessing the full potential of digital transformation and AI and associated technologies. The book employs a multidisciplinary approach, drawing from various fields such as computer science, sociology, philosophy, political science, economics, law and governance. It combines theoretical analysis, empirical case studies, and expert perspectives to provide a holistic view of the subject matter. This book caters to a diverse audience, including students, researchers, academics, policymakers, industry professionals, and technology enthusiasts. It provides a valuable resource for those seeking a comprehensive understanding ofthe opportunities and challenges arising from the integration of digital transformation and AI in society.
How will AI change our world within twenty years? A pioneering technologist and acclaimed writer team up for a “dazzling” (The New York Times) look at the future that “brims with intriguing insights” (Financial Times). This edition includes a new foreword by Kai-Fu Lee. A BEST BOOK OF THE YEAR: The Wall Street Journal, The Washington Post, Financial Times Long before the advent of ChatGPT, Kai-Fu Lee and Chen Qiufan understood the enormous potential of artificial intelligence to transform our daily lives. But even as the world wakes up to the power of AI, many of us still fail to grasp the big picture. Chatbots and large language models are only the beginning. In this “inspired collaboration” (The Wall Street Journal), Lee and Chen join forces to imagine our world in 2041 and how it will be shaped by AI. In ten gripping, globe-spanning short stories and accompanying commentary, their book introduces readers to an array of eye-opening settings and characters grappling with the new abundance and potential harms of AI technologies like deep learning, mixed reality, robotics, artificial general intelligence, and autonomous weapons.
Autonomous Vehicles: Technologies, Regulations, and Societal Impacts explores both the autonomous driving concepts and the key hardware and software enablers, Artificial intelligence tools, needed infrastructure, communication protocols, and interaction with non-autonomous vehicles. It analyses the impacts of autonomous driving using a scenario-based approach to quantify the effects on the overall economy and affected sectors. The book assess from a qualitative and quantitative approach, the future of autonomous driving, and the main drivers, challenges, and barriers. The book investigates whether individuals are ready to use advanced automated driving vehicles technology, and to what extent we as a society are prepared to accept highly automated vehicles on the road. Building on the technologies, opportunities, strengths, threats, and weaknesses, Autonomous Vehicles: Technologies, Regulations, and Societal Impacts discusses the needed frameworks for automated vehicles to move inside and around cities. The book concludes with a discussion on what in applications comes next, outlining the future research needs. - Broad, interdisciplinary and systematic coverage of the key issues in autonomous driving and vehicles - Examines technological impact on society, governance, and the economy as a whole - Includes foundational topical coverage, case studies, objectives, and glossary
